Data Science and symbolic AI: Synergies, challenges and opportunities IOS Press
Qualitative simulation, such as Benjamin Kuipers’s QSIM, approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove. We expect it to heat and possibly boil over, even though we may not know its temperature, its boiling point, or other details, such as atmospheric pressure. Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance. Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds. Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists.
The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems.
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Neuro-symbolic artificial intelligence can be defined as the subfield of artificial intelligence (AI) that combines neural and symbolic approaches. By symbolic we mean approaches that rely on the explicit representation of knowledge using formal languages—including formal logic—and the manipulation of language items (‘symbols’) by algorithms to achieve a goal. Since ancient times, humans have been obsessed with creating thinking machines.
The primary motivation behind Artificial Intelligence (AI) systems has always been to allow computers to mimic our behavior, to enable machines to think like us and act like us, to be like us. However, the methodology and the mindset of how we approach AI has gone through several phases throughout the years. “Our vision is to use neural networks as a bridge to get us to the symbolic domain,” Cox said, referring to work that IBM is exploring with its partners.
Common sense is not so common
Often it is intuition, insight or even “feeling” that alerts us, when logic is leading in the wrong direction. To be sure, “mind” should include, in addition to logical reasoning, memory and thinking – plus perception, feeling, emotion, intention, intuition, imagination and so on. While we have yet to build or re-create a mind in software, outside of the lowest-resolution abstractions that are modern neural networks, there are no shortage of computer scientists working on this effort right this moment. Since at least 1950, when Alan Turing’s famous “Computing Machinery and Intelligence” paper was first published in the journal Mind, computer scientists interested in artificial intelligence have been fascinated by the notion of coding the mind. The mind, so the theory goes, is substrate independent, meaning that its processing ability does not, by necessity, have to be attached to the wetware of the brain. We could upload minds to computers or, conceivably, build entirely new ones wholly in the world of software.
This processing power enabled Symbolic AI systems to take over manually exhaustive and mundane tasks quickly. Symbolic AI is more concerned with representing the problem in symbols and logical rules (our knowledge base) and then searching for potential solutions using logic. In Symbolic AI, we can think of logic as our problem-solving technique and symbols and rules as the means to represent our problem, the input to our problem-solving method. The natural question that arises now would be how one can get to logical computation from symbolism.
He lives in Berlin and travels frequently to Asia and elsewhere, consulting on economics, science and technology. “Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child brain is something like a notebook as one buys it from the stationer’s.
- Training an AI chatbot with a comprehensive knowledge base is crucial for enhancing its capabilities to understand and respond to user inquiries accurately and efficiently.
- The requirements of symbolic AI are that someone — or several someones — needs to be able to specify all the rules necessary to solve the problem.
- Not copy the way the brain works — we still don’t know enough about how the brain works to do that.
- Again, this stands in contrast to neural nets, which can link symbols to vectorized representations of the data, which are in turn just translations of raw sensory data.
- In order to tackle these types of problems, the researchers looked for a more data-driven approach and because of the same reason, the popularity of neural networks reached its peak.
Kahneman describes human thinking as having two components, System 1 and System 2. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed. The distinction between symbolic (explicit, rule-based) artificial intelligence and subsymbolic (e.g. neural networks that learn) artificial intelligence was somewhat challenging to convey to non–computer science students. Neuro-symbolic AI is a synergistic integration of knowledge representation (KR) and machine learning (ML) leading to improvements in scalability, efficiency, and explainability.
Another recent example of logical inferencing is a system based on the physical activity guidelines provided by the World Health Organization (WHO). Since the procedures are explicit representations (already written down and formalized), Symbolic AI is the best tool for the job. When given a user profile, the AI can evaluate whether the user adheres to these guidelines. Although Symbolic AI paradigms can learn new logical rules independently, providing an input knowledge base that comprehensively represents the problem is essential and challenging. The symbolic representations required for reasoning must be predefined and manually fed to the system.
For instance, if you take a picture of your cat from a somewhat different angle, the program will fail. A similar problem, called the in trying to enumerate the preconditions for an action to succeed. An infinite number of pathological conditions can be imagined, e.g., a banana in a tailpipe could prevent a car from operating correctly. The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to create plans.
How neuro-symbolic AI might finally make machines reason like humans
In symbolic reasoning, the rules are created through human intervention and then hard-coded into a static program. So to summarize, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens. Another common application of symbolic AI is knowledge representation. Knowledge representation algorithms are used to store and retrieve information from a knowledge base. Knowledge representation is used in a variety of applications, including expert systems and decision support systems.
Being the first major revolution in AI, Symbolic AI has been applied to many applications – some with more success than others. Despite the proven limitations we discussed, Symbolic AI systems have laid the groundwork for current AI technologies. This is not to say that Symbolic AI is wholly forgotten or no longer used. On the contrary, there are still prominent applications that rely on Symbolic AI to this day and age.
IBM, MIT and Harvard release “Common Sense AI” dataset at ICML 2021
NLP is used in a variety of applications, including machine translation, question answering, and information retrieval. One such project is the Neuro-Symbolic Concept Learner (NSCL), a hybrid AI system developed by the MIT-IBM Watson AI Lab. This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. There have been several efforts to create complicated symbolic AI systems that encompass the multitudes of rules of certain domains. Called expert systems, these symbolic AI models use hardcoded knowledge and rules to tackle complicated tasks such as medical diagnosis.
Even if the AI can learn these new logical rules, the new rules would sit on top of the older (potentially invalid) rules due to their monotonic nature. As a result, most Symbolic AI paradigms would require completely remodeling their knowledge base to eliminate outdated knowledge. For this reason, Symbolic AI systems are limited in updating their knowledge and have trouble making sense of unstructured data.
In contrast, using symbolic AI lets you easily identify issues and adapt rules, saving time and resources. Naturally, this does not mean that AI has not made progress in other respects. On the contrary, AI has achieved tremendous breakthroughs in areas such as expert systems, speech and pattern recognition, automatic translation, control of complex technical processes, assessment of medical data and robotics. The fact that it sounds as if it is is proof positive of just how simple it actually is. It’s the kind of question that a preschooler could most likely answer with ease.
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